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Mobile Artificial Intelligence Projects

Mobile Artificial Intelligence Projects

By : NG, Padmanabhan, Matt Cole
5 (1)
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Mobile Artificial Intelligence Projects

Mobile Artificial Intelligence Projects

5 (1)
By: NG, Padmanabhan, Matt Cole

Overview of this book

We’re witnessing a revolution in Artificial Intelligence, thanks to breakthroughs in deep learning. Mobile Artificial Intelligence Projects empowers you to take part in this revolution by applying Artificial Intelligence (AI) techniques to design applications for natural language processing (NLP), robotics, and computer vision. This book teaches you to harness the power of AI in mobile applications along with learning the core functions of NLP, neural networks, deep learning, and mobile vision. It features a range of projects, covering tasks such as real-estate price prediction, recognizing hand-written digits, predicting car damage, and sentiment analysis. You will learn to utilize NLP and machine learning algorithms to make applications more predictive, proactive, and capable of making autonomous decisions with less human input. In the concluding chapters, you will work with popular libraries, such as TensorFlow Lite, CoreML, and PyTorch across Android and iOS platforms. By the end of this book, you will have developed exciting and more intuitive mobile applications that deliver a customized and more personalized experience to users.
Table of Contents (12 chapters)
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6
PyTorch Experiments on NLP and RNN
7
TensorFlow on Mobile with Speech-to-Text with the WaveNet Model
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8
Implementing GANs to Recognize Handwritten Digits

TensorFlow on Mobile with Speech-to-Text with the WaveNet Model

In this chapter, we are going to learn how to convert audio to text using the WaveNet model. We will then build a model that will take audio and convert it into text using an Android application.

This chapter is based on the WaveNet: A Generative Model for Raw Audio paper, by Aaron van den Oord, Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Vinyals, Alex Graves, Nal Kalchbrenner, Andrew Senior, and Koray Kavukcuoglu. You can find this paper at https://arxiv.org/abs/1609.03499.

In this chapter, we will cover the following topics:

  • WaveNet and how it works
  • The WaveNet architecture
  • Building a model using WaveNet
  • Preprocessing datasets
  • Training the WaveNet network
  • Transforming a speech WAV file into English text
  • Building an Android application

Let's dig deeper into what Wavenet actually is.

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